https://github.com/ledell/h2oensemble-benchmarks
Benchmarks of the H2O Ensemble R interface (H2O 2.0).
https://github.com/ledell/h2oensemble-benchmarks
Last synced: 9 months ago
JSON representation
Benchmarks of the H2O Ensemble R interface (H2O 2.0).
- Host: GitHub
- URL: https://github.com/ledell/h2oensemble-benchmarks
- Owner: ledell
- Created: 2014-11-17T08:25:08.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2020-11-04T20:27:41.000Z (over 5 years ago)
- Last Synced: 2025-02-02T01:33:04.625Z (over 1 year ago)
- Language: R
- Homepage:
- Size: 37.1 KB
- Stars: 14
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# h2oEnsemble Benchmarks (H2O 2.0)
**Note:** These benchmarks are for [H2O 2.0](https://github.com/h2oai/h2o-2) (aka. "H2O Classic"). The current version of H2O is [H2O 3.0](https://github.com/h2oai/h2o-3). There are no plans to update this benchmark at this time.
This repository provides a framework for benchmarking the **h2oEnsemble** package. The **h2oEnsemble** package provides an R API to train ensembles of [H2O](https://github.com/h2oai/h2o-2) machine learning algorithms. You can read more about the "H2O Ensemble" project on its [GitHub page](https://github.com/h2oai/h2o-2/tree/master/R/ensemble). Note that as of H2O 3.10.3.1 (Jan 30, 2017), Stacked Ensembles are now available as part of base H2O so the **h2oEnsemble** package is not longer "needed" and hence no longer maintained.
- An example binary classification benchmark is provided in the `twoClass/higgs` folder.
- To use the software on an Amazon EC2 cluster, follow the instructions in the `ec2/README.md` file to set up the cluster.
- After you set up the cluster, you should `cd` to a specific benchmark directory and execute the benchmark. For example, the `higgs` benchmark can be executed as follows:
```
cd src/twoClass/higgs
nohup ./run-bench.sh &
```
- If you choose to use a single node instead of a multi-node cluster, you can skip the EC2 steps. If a multi-node cluster is not available, the code will create a single node multicore cluster using all the cores available on your machine.
- If the benchmark is not executed using the H2O Ubuntu AMI, you may have to change the path of the `BENCH_ROOT` variable in the scripts to point to the location of this repository on your machine.